Become a data-driven organization with the Internet of Things Executive summary Personal health monitors tracking your fitness, trashcans monitoring their fullness, watches telling you more than just the time, and agricultural soil monitors saying it’s time to water. It seems a day doesn’t go by that we don’t hear about the latest “offline” thing, device, or equipment becoming “online,” moving from isolation to being connected to the Internet of Things (IoT). It’s clear that integrating sensors, electronics, and network connectivity into devices can enable innovation, enhancing and extending the way we work and interact with each other and the world around us.

1. Business white paper
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Become a data-driven organization with the Internet of Things
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2. Executive summary
Personal health monitors tracking your fitness, trashcans monitoring their fullness, watches telling you more
than just the time, and agricultural soil monitors saying it’s time to water. It seems a day doesn’t go by that
we don’t hear about the latest “offline” thing, device, or equipment becoming “online,” moving from isolation
to being connected to the Internet of Things (IoT). It’s clear that integrating sensors, electronics, and
network connectivity into devices can enable innovation, enhancing and extending the way we work and
interact with each other and the world around us.
McKinsey estimates that IoT has a total potential economic
impact of $3.9 trillion USD to $11.1 trillion USD a year by 2025.1
Business white paper
1
The Internet of Things: Mapping the Value Beyond the Hype, McKinsey Global Institute, June 2015.
Executive summary
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opportunities
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3. Gartner estimates a potential of 26 billion connected devices by 2020.2
Morgan Stanley even estimates a
potential of 75 billion connected devices by 2020.3
More important than the device estimate, though, are
the possible industry transformations and business outcomes. So, the questions we should ask ourselves as
business leaders, technologists, and entrepreneurs are:
Global sensor and device connectivity presents countless opportunities for transformation of businesses
and industries, disrupting the status quo and existing marketplaces. Companies should capitalize on the
opportunity, but need to do it smartly—mitigating financial and operational risks. So, understanding how value
can be created from IoT, identifying the business opportunities, and understanding the challenges and the
technologies needed to mitigate them are the keys to success.
With our long-standing experience and expertise as a trusted technology partner to enterprises across
every industry globally, we will guide you through these important questions so you can maximize the
benefit and minimize the risk from IoT investments. In this paper, we’ll share our perspectives on the value,
the business opportunities, the technology challenges, and the ideal partner to seize the opportunity.
Business white paper
2
Forecast: The Internet of Things, Worldwide, 2013, Gartner, November 2013.
3
Morgan Stanley: 75 Billion Devices Will Be Connected To The Internet Of Things By 2020, Business Insider, Tony Danova, October 2013.
Where is the value? What are the business
opportunities?
What are the challenges to
unlock the promise of IoT?
Executive summary
The value
The business
opportunities
Your partner
The challenges
Where to start

4. The value
In order to justify the investment in RD, infrastructure, and governance required for IoT, senior executives
are demanding to understand the value to the business. In our experience, value can be expressed across
three dimensions—contextual, integrated, and operational.
It should come as no surprise that the primary value of IoT is in the data, more specifically, the enhanced
decision-making and automation that arise from the convergence of analytical insights, enabled by
connected devices, with people. IDC predicts IoT data will account for 10 percent of the world’s data by
2020, of about 44 zettabytes.4
Clearly this is Big Data. But what can this data tell us that we didn’t already
know and how can we use this information to improve our well-being and optimize businesses?
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4
The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things, IDC, April 2014.
Executive summary
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The business
opportunities
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5. Contextual insights
Your health-monitoring wristband, the vending machine at the mall, aircraft engine serial number 9AB429—
these are all examples of devices that are being instrumented with sensors and connected. What is
important to point out about these and every sensor-enabled connected device is the notion of contextual
data, the data each device creates, and contextual insights, the inference that can be made about the
device and environment through analysis of the data. With contextual data, we can start gaining insights and
creating value where previously not possible.
Let’s take, for instance, your health-monitoring wristband. Embedded into this device is an accelerometer,
which is used to sense movement. As you move, contextual data about your personal activity, such as
calories burned, steps taken, and your heart rate is measured, stored, and presented to you through an app
so you can monitor, maintain, and improve your personal health fitness. Should you miss a few sessions at
the gym, data analytics can create contextual insights, determining this trend and having the device send
you an encouraging text that it’s time for a run, helping improve your well-being.
Vending machines can be embedded with pressure sensors and counters collecting contextual data on
real-time inventory levels, which are made available to an operations manager or fed directly into an order
management system. When inventory reaches a specified threshold, local distribution can be automatically
dispatched to refill, ensuring no loss in revenue from stock outs and optimizing profit for the business.
Big Data analytics can even enable contextual insights on historical buying patterns and consumer
preferences, which lead to improved demand forecasting and product planning.
Across all industries, there are billions of devices and equipment essential for operations. For instance, in the
aviation industry, aircraft engines have a vital role in the operations of the airline. Airlines rely on the uptime
and performance of their engines and any unplanned downtime (commonly referred to as “time off wing”)
causes delays to flights—which we all know can lead to a domino effect—negatively impacting customer
experiences and future revenue.
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6. Unplanned downtime costs a liquid natural gas (LNG) facility on
average about $150 million USD annually.5
By instrumenting industrial equipment such as engines, pumps, valves, and other critical assets with sensors
and collecting and monitoring contextual data such as temperature, power, and pressure, enterprises can
have the data they need to analyze and gain insights about the equipment (status, performance, and health)
while in operation. By analyzing the data in real time, enterprises can have contextual insights delivered
through advanced notification of potential problems in their equipment. So, in turn, unplanned “time off
wing” for the airlines can be minimized, improving operations and revenue, and, of course, getting us to our
destination safely and on time.
Contextual insights are only one part of the potential value of IoT. When we start considering how individual
devices and equipment fit in an ecosystem of other connected devices within the business operations, an
even larger opportunity for value creation can be possible.
Integrated intelligence
Let’s look back to the personal health-monitoring example. While there is value for each person in tracking
their own health metrics, imagine the possibilities when you start combining and correlating data across
disparate data sets and large populations, creating integrated intelligence. For instance, healthcare
providers aspire to provide the highest quality of care for their patients (while, of course, maintaining their
balance sheet). If doctors had access to their patients’ personal health-monitoring data, they could have a
much better understanding of the historical and real-time health and wellness of all their patients and even
populations. There could be a future where treatments and prescription dosages are personalized and
precise based on real-time conditions of their patients and the environment. Through a Big Data analytics
platform, concerning trends in your blood pressure or other measurement could be uncovered and your
doctor could adjust your treatment. This data could even be of interest to pharmaceutical companies to
better understand drug interaction with environmental impacts and human activity.
Business white paper
5
Jeff Immelt on GE Oil and Gas Strategy and the Power of One Percent, ARC Advisory Group, May 2014.
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7. More visibility means better operational intelligence.
Additionally, although contextual insights on each device or equipment are valuable, enterprises and
industries have more than one type of equipment critical for operations. For instance, an oil field consists of
compressors, pumps, heat exchangers, drilling rigs, artificial lifts, and more. Operators rely upon the uptime
and performance for all equipment. They also rely upon integrated intelligence, through predictive failure
notification, so that visibility into the entire production fleet will maximize operational gain.
Today, a typical oil-drilling platform might contain 30,000 sensors,
but only 1 percent is actually used for decisions.6
In transportation, automotive companies are instrumenting a vast number of sensors that provide data on
each vehicle and the environment around it. VTT Technical Research Centre of Finland has developed a
“slipperiness detection” system for vehicles utilizing standard anti-lock brake system sensors.7
By analyzing
the contextual data from the sensors, VTT can infer the slipperiness of the road based on friction, creating
contextual insights on the road condition. As contextual insights, the system can warn the driver, avoiding a
potential accident. Additionally, with respect to integrated intelligence, every vehicle across a city can report
on its environment and in aggregate we can have citywide situational awareness with endless opportunities
for transportation optimization.
Analyzing multiple discrete data sources can even uncover correlations, interdependencies, and patterns
that lead to new insights, which is not possible by analyzing sensors independently.
Business white paper
6
Unlocking the potential of the Internet of Things, McKinsey Global Institute, June 2015.
7
Slipperiness detection system, VTT Technical Research Centre of Finland, December 2013.
Executive summary
The value
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opportunities
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The challenges
Where to start

8. For instance, looking again at transportation. On-board diagnostics from a vehicle can collect data on
driving behavior. Just that information alone can be used to determine an insurance rating for a driver. But
by adding in information such as the car’s location, the degree of traffic on the road, the weather conditions,
and the driver’s schedule for the day and heart rate, insurance companies might infer from the integrated
intelligence that a stressed driver with a busy schedule is more likely to have an accident and provide “on
the spot” incentives—such as a rebate to drive slower or arrive at his next appointment 5 minutes late.
Meanwhile, oil refineries would prefer processing discounted crude instead of premium crude for the
financial savings, but doing so can potentially lead to corrosion and scaling of critical equipment, which are
detrimental to the expensive assets such as cooling towers and boilers. Refineries today are instrumenting
their production process to monitor water quality, flow rate, pressure, and more. A Big Data analytics
platform has the potential to find patterns across the various data sets to predict equipment degradation
based on historical data. So an optimal balance between processing discounted crude and equipment
performance for maximum financial gain can be achieved. That’s business optimization.
Operationalizing insights
New data from IoT, the analytical insights discovered, and the technology investments would be at a loss if
the insights are not actionable and delivered to the right people and systems across the enterprise at the
right time and in the right format. Therefore, it is essential that actionable insights are integrated within
business operations and for all stakeholders.
In the vending machine example, who should receive the stock out insights—local distribution for re-supply,
fleet managers for planning delivery routes, or brand managers for understanding customer preferences?
Clearly, all of them. However, given they have different roles that impact the business operations differently,
each person needs a different view of the insights. Brand managers may want historical dashboards on their
Web browser, local distribution may want predictive alerts summarizing total supply across their geography,
and fleet managers may want real-time status on a geographic information system (GIS) map overlaid with
driver GPS locations.
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9. Let’s say there is a low supply at the stadium before a big game. An application tracks the priority events,
locations, and supply levels in real time. An operations manager, who is utilizing the dashboard, can make
better decisions about how to manage inventory levels, then relay this information to drivers through tablets
to re-route, optimizing business value. Maximizing the return on investments in a Big Data platform and IoT
technologies require delivering the actionable insights to the people and systems that can best leverage
them as well as having the right enabling technologies and tools to do so.
Through connectivity and increased automation, devices such as smart utility meters or streetlights can
be remotely monitored and controlled. IoT enables us to take operations that were formerly labor intensive,
requiring physical inspection or actuation, in sometimes hazardous environments and reimagine them in
entirely new ways.
Real-time, automated control can even further increase the value of insights. In some scenarios, with the
right confidence and security protocols, equipment and processes can self-adjust based on analytics. By
integrating multiple data sources, integrated intelligence can enable real-time, closed feedback loops. For
instance, General Electric and Siemens are manufacturers of wind turbines and by analyzing wind speed
and direction in real time, their turbines can self-adjust their pitch and rotor blade angles, maximizing
energy production.
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10. The business opportunities
By now, you can see the tremendous opportunity to disrupt the status quo and transform industries and
markets with the Internet of Things. By becoming data-driven organizations, companies can capitalize on
the opportunity and create new value for themselves, their customers, and their partners. While companies
of all shapes and sizes can realize new business outcomes with IoT, here are three types that we see as
having much to gain in the near term and why:
Equipment manufacturers
Equipment manufacturers have a tremendous opportunity to leverage IoT to grow their business with improved
or new products, services, and better customer experiences. Customers expect quality-made products and
competition continues to undercut price, impacting profit margins. So competitive advantages and differentiation
are essential for growth. IoT with a Big Data analytics platform is the opportunity to have both.
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11. For instance, by integrating sensors that monitor the performance and use of installed equipment,
manufacturers can create value with historical and real-time data analysis, generating contextual insights.
These insights help to understand the products better, how they are utilized, and how they perform. It
even predicts problems before they arise. The insights can be sold as software application offerings to their
customers or even packaged in contracts, enabling the manufacturer to offer service-level agreements
guaranteeing the performance of the equipment. That’s business model innovation.
Additionally, historical data from usage across a fleet of products dispersed in the field can help the
manufacturer better understand condition-based impact and the insights can be fed back into product
development for improved design and competitive advantages.
Arguably, IoT’s greatest value comes from the broader ecosystem built by both suppliers and customers
atop the data and devices. This ecosystem creates competitive differentiation that moves beyond the cost to
focus on value creation and long-term customer and supplier collaboration.
Manufacturers can now differentiate their offerings from a onetime capital purchase to new and improved
products and services. They can even enable new business models. Take for instance, the connected
printers from HP Inc. By integrating sensors into the ink cartridges, HP Inc. can automatically ship ink based
on real-time use, ensuring customers don’t run out of supply and enable an innovative “ink as a service”
business model.
Business white paper
Figure 1: HP Inc. connected printers: Business transformation
Connected printer
Connects to the cloud
Supply chain
Optimized and delivers ink
before running out
Smart ink cartridges
Sensors inform when ink is
getting low
Your printer tells us
when to send ink
Customer outcomes
• Never run out of ink
• Provide user satisfaction
• Get financial savings
Business outcomes
• Increased operating income
• Improved financial forecasting
• Improved supply chain operationsE-commerce
Seamless integration to billing
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opportunities
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12. Enterprises
Enterprises have so much to gain from IoT and Big Data analytics. The unpredictable nature of equipment
can make it a liability, but with new insights from contextual data, equipment can truly be an asset.
Instrumentation and sensor data collection across operations can provide visibility and light to what
was previously dark. Enterprises with real-time, historical, and predictive insight to all their devices and
equipment can ensure their businesses are operating at peak levels for maximum financial gain and lowest
risk. Not only is the integrated intelligence from devices and equipment valuable, but also discovering how
these new sources of data can integrate within a larger Big Data strategy unlocks even more value.
For instance, in retail the mission is clear—provide the best customer experience across multiple channels
to increase customer loyalty and grow the business. Retailers face countless pressures including stock
outs, competitive prices, and workforce productivity. New data from IoT—customer mobile phones
with GPS, video cameras, location positioning such as iBeacon, weather, and RFID tags—analyzed with
transactional data creates integrated intelligence that improves customer experiences with seamless
customer service across all channels. Optimized multi-channel operations from understanding intent to
purchase, identifying cross-sell opportunities, improving product placement and workforce productivity,
and optimizing inventory and pricing can increase customer loyalty.
In the oil and gas industry, operators are responsible for ensuring safe and efficient operations across their entire
supply chain from drilling, transportation, refinement, and distribution. Globally, there are more than 183,000 miles
of crude oil pipelines, 155,000 miles of petroleum product pipelines, and 600,000 miles of natural gas pipelines
transporting highly volatile substances.8
More and more disparate data sources such as high-pressure water
pumps, flow meters, in-line inspection devices, and aerial surveys providing information about the performance
and operation of pipelines are becoming available to operators. The sensor data from pipelines integrated with
other operational databases enabled by a Big Data analytics platform can provide operators with integrated
intelligence of their operations for maximum efficiency and safety of employees and the community.
Business white paper
8
Pipeline Industry Growth Fueled By Increasing Global Energy Demand, Shale Gas Exploration, Pipeline and Gas Journal (Ganesh Dabholkar), March 2014.
Executive summary
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13. Service-based businesses
Across all industries, there is a wide-range of service-based businesses providing services from equipment
services, engineering services, integration services, software applications, business and operations consultancy
to professional services that extend an enterprise’s workforce. Their goals are the same—provide superior
service and solutions that maximize their customers’ business outcomes.
The IoT promises to solve significant challenges and create new business value. But with any new technology
investment, there may be concerns of the actual value achieved falling short of expectations. This can limit
technology adoption and hinder the investments that can drive outcomes. Service-based businesses are often
experts in their customers’ business. They understand the challenges and can leverage their domain expertise
with Big Data analytics. The expertise of both Big Data and the company’s business model often lower the
barriers to technology adoption, and creates growth opportunities for their customers and themselves.
For instance, companies servicing healthcare equipment or providing services to an oil field can improve
its competitive differentiation by offering solutions. They can offer IoT-enabled solutions that monitor
or diagnose remotely, manage assets, support in real-time, proactively maintain, and optimize across all
operations—packaged in innovative business models.
Here are just some of the business outcomes possible with the right enabling technologies:
• Increased revenue
• Optimized operations
• New products and services
• Workforce productivity
• Reduced risks
• Reduced operating cost
• Optimized maintenance
• Optimized network throughput
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The challenges
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14. The challenges
Unlocking the value to the Internet of Things doesn’t come without challenges and requires business
leaders to partner with their organizations’ technology leaders. Creating intelligent devices with sensors
and processing as well as enabling secure and reliable connectivity are just the beginning. Enabling secure
data, managing the data, processing insights, timely insight delivery, and cost management are some of the
big challenges.
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15. Technology leaders need to be at the center of the enterprise leading the company and overcoming the
hurdles for the business. The following table contains significant business challenges with corresponding
technology requirements to maximize the value and minimize the financial and operational risks from IoT:
Business white paper
Table 1: The business challenges and technology requirements for the Internet of Things
Business challenges Technology requirements
Delivering business value
when it’s needed
• Balancing compute at network edge and cloud to optimize latency, insights, and TCO
• Batch, real-time, and interactive analytics
• Flexible languages for writing queries, scripts, or machine learning
• Flexible schemas
• Predictive analytics
• Multiple data sources and aggregating data lakes
• Enabling multiple technology stakeholders—BI analysts, programmers, data scientists
• Access to apps when device or user is offline
Delivering business value
where it’s needed
• Operationalizing the insights in real time, closed loop to operations
• Multi-tenancy
• Environment for agile application development
• Enabling the various stakeholders and departments that need the insights
• Flexible app deployment—Web and mobile platforms
• Connectors to multiple business intelligence tools
Securing and governing the
data, apps, and users end
to end
• Managing cybersecurity threats with new data from sensitive equipment
• Securing data across the network and cloud
• Securing control systems from intruders
• Compliance and regulatory requirements from data
• Regulation and authorization of applications and data
• Managing data lifecycle
• Privacy of sensitive data
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16. Business white paper
Table 1: The business challenges and technology requirements for the
Internet of Things (continued)
Storing and analyzing
massive data at scale
• 100 percent of data structured and unstructured with exponential growth
• High-velocity data
• Accessibility, performance, and speed for multiple applications
• Flexible, scale-out infrastructure
• Minimizing total cost of ownership (TCO)
• Control of IT operations, but enablement of data
• Leveraging existing investments, but optimizing for new value potential and future requirements
• Minimizing data center footprint and energy consumption
• Open, standards-based architecture
Breaking down the
data silos
• Analytics across multiple data types and sources
• Single pane view and dashboard of all critical assets for operations, consolidated across multiple
vendors
• Data ownership and access for new IoT data
• Connectors to large ecosystem of data types
• Data lake across multiple clouds
• Open APIs
These challenges directly impact the viability of business outcomes and financial return from investments in IoT.
In consideration of this long list, technology leaders can’t do it alone and need a partner who understands the
business challenges, technology requirements, and has end-to-end capabilities, solutions, services, and partner
ecosystems to help deliver the business outcomes.
Hewlett Packard Enterprise studied the top home security
systems using HPE Fortify On Demand and found 100 percent
displayed significant security deficiencies.9
9
HP (now Hewlett Packard Enterprise) Study Finds Alarming Vulnerabilities with Internet of Things
(IoT) Home Security Systems, HP (now Hewlett Packard Enterprise), February 2015.
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17. Your partner
As your trusted partner for enterprise information technology, Hewlett Packard Enterprise has a
comprehensive portfolio of infrastructure hardware, software, and services and a partner ecosystem to
enable your organization to unlock the value of the IoT across all industries. Hewlett Packard Enterprise
is uniquely positioned with proven experience, leadership, technology breadth, and has complete
understanding of the challenges with integrated solutions needed to maximize the value and minimize the
risks from IoT.
The HPE IoT Reference Model is a functional view of our extensive portfolio of products, solutions, and
services for IoT from connectivity of devices to delivering business outcomes, securely and efficiently
at scale, across the technology stack. Utilizing a data-centric approach, the HPE IoT Reference Model
integrates best-in-class Hewlett Packard Enterprise technologies and services from the edge, network, and
core to deliver actionable insights and business outcomes from IoT.
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18. Solution highlights:
Big Data and analytics platform for IoT
With the ever-growing proliferation of data available to organizations, Hewlett Packard Enterprise recognizes
that the industry needs an enterprise-grade software platform that can ingest, aggregate, manage, and
analyze all of your data. That platform should also deliver insights at the scale and speed required by your
organization. HPE Haven is a pioneering Big Data platform that can harness all data types, ingesting data
from multiple, disparate sources, and analyze it at the speed and scale of enterprise needs. Whether the data
is structured transactional business, machine, and sensor data or unstructured human information such as
video, audio, and text, Hewlett Packard Enterprise has the storage and analytics processing capabilities to
deliver value on the data.
Across industries, speed of insights makes the difference in competitive advantages, business outcomes,
and even safety. In oil and gas, each in-line inspection devices (known as “pigs”) generates terabytes of data,
measuring and monitoring for potential cracks and leaks throughout a pipeline.
Business white paper
Figure 2: HPE IoT Reference Model
Devices
Edge Network Core Industries
Applications
Data
Big Data and
analytics platform
Security
Services
Business
intelligence
Data
integration
Network
Data
Wireless/Wired/
Remote
Device and service
management
Network interworking
Data acquisition and
verification
Distributed
mesh
computing
Infrastructure
• Compute
• Storage
Analytics
Governance Backup
recovery
Cloud application platform
Cloud management orchestration
Infrastructure—Compute | Storage | Network
Archiving Records
management
Master data
management
Apps Infrastructure
Application ESM/SIEM
Authentication/
Encryption
Real-time
analytics
Batch analytics
Advanced
analytics
Context
Visualization/
Reporting
Vertical apps
Operations
management
Manufacturing
Retail
Healthcare
Oil Gas
Transportation
Financial
service
Public sector
Utilities
Aviation
....
Information governance and management
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19. While pigs are intended to deliver actionable insights into the pipeline health and integrity, the Wall Street
Journal reported that analyzing the reams of data collected can take months10
using traditional technologies,
which is too long when safety is on the line. In drilling, a single oil well can generate one terabyte of
production data daily, but it has been reported that engineers spend 60 percent of their time mining data.11
Imagine if a software platform can mine data quickly, delivering insights in real time. Now engineers can
focus on data science and operationalizing the intelligence. With HPE Haven, you have a platform capable of
delivering actionable insights for all your data at enterprise-grade performance.
In a recent analytics benchmark study, HPE Vertica ingested,
stored, and analyzed 22.8 trillion rows of smart meter data at an
unprecedented speed and in the smallest hardware footprint in
the industry.12
Case study: Trane, a leading global provider of indoor comfort solutions and services and a brand of
Ingersoll Rand, partnered with Hewlett Packard Enterprise for an equipment data analytics solution. The
Trane Intelligent Services group offers offsite monitoring, event mitigation, and energy performance services
to improve the efficiency and reliability of installed systems and helps their customers reduce energy and
maintenance costs. As this group has grown, Trane recognized a need for a data platform to handle the
increased analytics workloads and turned to HPE Vertica for a solution. HPE Vertica presented an efficient
platform that accommodates time series data, is horizontally scalable, enables flexible cloud deployment,
and offers superior compression and speed. Trane Intelligent Services is now utilizing HPE Vertica for
30 sites, in hundreds of buildings and expects to expand this service for 700 sites, a total of over 1,000
buildings worldwide.
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From PDF
10
The Wall Street Journal, Oil-Pipeline Cracks Evading Robotic “Smart Pigs,” August 16, 2013.
11
“Innovation and Growth in the Oil Gas Industry,” Oil Gas Monitor, John Brantley, May 29, 2012.
12
IoTAbench: an Internet of Things Analytics benchmark, HP (now Hewlett Packard Enterprise), December 2014.
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The challenges
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20. Built on an open platform with a flexible architecture, HPE Haven integrates with all the common Hadoop
distributions (Hortonworks, Cloudera, and MapR) and over 500 industry connectors to hundreds of content
repositories. It leverages common languages for developers, business analysts, and data scientists to write
SQL queries, scripts, or develop predictive machine-learning algorithms. Open APIs and our vast ecosystem
enable partners to build advanced analytics and applications on top of HPE Haven. Additionally, HPE Haven
is available on-premises or in the cloud, providing flexible deployment options.
The HPE Haven platform includes three analytics engines:
• HPE Vertica is a massively scalable database platform, purpose-built from the ground up for real-time
analytics from terabyte to petabyte data sets on industry-standard hardware. As a distributed, columnar
database, HPE Vertica delivers insights 50 to 100 times faster than traditional relational databases and
easily scales out with growing datasets on industry-standard servers, storage, and networking. Built-in
compression reduces infrastructure cost while dramatically increasing performance. HPE Vertica is an
efficient database for real-time loading and querying of high volume time-series data for sensor and
machine data.
• Distributed R is an HPE open source framework for predictive analytics, enabling scale-out, parallel
processing of the popular R language for developing machine-learning algorithms. Distributed R provides
data scientists with a platform to create predictive algorithms from large, petabyte-sized datasets.
Organizations can develop predictive foresight its big datasets originating from operations, equipment
performance, or IoT. Additionally, Distributed R can be utilized on industry-standard hardware, which
eliminates rework of standard R libraries that support massive scale out.
• HPE IDOL is our analytics platform for unstructured data, powering analytics, information management,
and governance solutions by enabling enterprises to categorize, index, search, and analyze human
information at scale with context. It can process more than 1,000 file types including video, images, audio,
email, and social media. Hewlett Packard Enterprise has a tremendous portfolio in advanced video and
image processing analytics. Traditionally, industry use of video analytics has been limited to surveillance.
However, our deep expertise of video and image analytics solutions coupled with our Big Data platform
can enable even greater business and operations optimization across all industries.
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The challenges
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21. Infrastructure for IoT
To lead in this market opportunity, technology leaders need to be agile and focus resources, human, and
capital, on delivering business outcomes that will lead to competitive advantages and breakout growth. We
must drive agility, cost control, and performance while ensuring the highest levels of security, throughout
our infrastructure and platforms.
We recognized that companies need infrastructure and architectures that are flexible and enable the enterprise
to be agile, delivering data insights to the right people at the right time at the right ROI. High availability,
performance, latency, scale, cost, and security need to be optimized so the promise of IoT is delivered.
For the edge: Speed and value of insights, capital costs, and operating costs are critical technology
implications. Sensors and devices in remote locations may have inadequate bandwidth available for the data
to move and the volume of data produced on the edge may be too large and cost prohibitive to move. So
there should be a balance of edge and cloud data processing based on the analytics, data sets, performance,
and costs. A distributed compute platform optimizes both performance and costs. When near-equipment
processing is feasible, analytics can be delivered faster and at a lower cost than transmitting and processing
all data to the cloud. The Hewlett Packard Enterprise infrastructure portfolio includes server technologies
optimized for edge—including HPE Moonshot and HPE ProLiant MicroServer Gen8.
For the network: The promise of insights derived from IoT is tremendous, but the data moving across the
communication network must be accessible and trusted to be reliably used. Today’s IoT isn’t trustworthy
with increasing threats to the authenticity and integrity of devices, data, applications, and networks.
HPE networking solutions address these issues and enable IoT to be a reliable element of business-critical
decisions and processes. The solutions enable a secure, scalable, and flexible network infrastructure that
unifies access and enables secure connectivity to all devices in virtually any operating environment. So
whether you need an explosion-proof wireless solution for an oil rig, a secured wired LAN for factory
automation, or remote VPN with cellular backhaul for supervisory control and data acquisition (SCADA)—
Hewlett Packard Enterprise has you covered with a network that is reliable, scalable, and trustworthy.
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22. Another challenge at the network is the heterogeneity of devices that need to be connected, configured, and
managed. Hewlett Packard Enterprise provides an end-to-end remote management function for mobile and
wireless devices that include dynamic device discovery, over-the-air device configuration, and fine-grained
control of IoT traffic to and from the device. Via this device, vendor independent and connectivity agnostic
function, millions of IoT devices for smart applications can be remotely managed on the same multi-tenant
platform in the cloud. The solution operates at a low total cost of ownership with high scalability and flexibility
due to the network interworking proxy and oneM2M/OMA-DM standards compliance.
Case study: The city of Auckland, New Zealand selected Hewlett Packard Enterprise to deliver a visionary
Big Data project designed to provide a safer community and more efficient roadways for its citizens.
Auckland Transport, Auckland’s government agency responsible for all of its transportation infrastructure
and services, deployed video analytics powered by HPE IDOL on servers and storage from HPE Enterprise
Group, and with support from HPE Software Professional Services. Auckland Transport uses HPE Haven to
analyze, understand, and act on vast quantities of data of virtually any type including text, images, audio,
and real-time video. The system leverages data from a variety of sources, including thousands of security
and traffic management cameras, a vast network of road and environmental sensors, as well as real-time
social media and news feeds.
“The safety and well-being of our citizens is always our top priority
and the Future Cities initiative is a big step in the right direction. Only
Hewlett Packard Enterprise could comprehensively deliver the
custom solution, expertise, and ecosystem at this scale to transform
our vision into reality.”13
– Roger Jones, CIO Auckland Transport
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13
City of Auckland, New Zealand Selects HP (now Hewlett Packard Enterprise) to Drive Groundbreaking
Future Cities Initiative, HP (now Hewlett Packard Enterprise), September 2014.
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23. For the core: HPE servers, storage, and networking technologies, architected for Big Data and IoT, serve
as our core infrastructure to empowering a data-driven organization to scale out and scale up. Based on
open standards, the HPE server portfolio includes industry-leading x86 servers optimized for Big Data
workloads. The recently published HPE Big Data reference architecture for Hadoop increases performance
and minimizes the cost of infrastructure and management.
For hybrid infrastructure: HPE Helion is our unified portfolio of products and services that makes it
easy for your organization to build, manage, and deploy applications in a hybrid IT environment. Within
HPE Helion, HPE CloudSystem is a fully integrated, end-to-end, private cloud solution built for traditional
and cloud-native workloads, and delivering automation, orchestration, and control across multiple clouds.
Cloud application platform for IoT
Equipment manufacturers, enterprises, and service-based businesses can quickly develop their own private
cloud platform, deploying IoT cloud applications to internal and external customers through the HPE Helion
Development Platform. HPE Helion Development Platform is a cloud application platform built on open-source
technologies that makes it faster to develop, deploy, and deliver highly available and scalable cloud-native
applications.
Architectural decisions for IoT applications must provide developers flexibility and instant access to
environments that support agile development and evolving DevOps practices. With this developer-centric
solution, your teams will be able to deploy applications faster, enabling accelerated innovation, decreasing
time to business outcomes, and optimizing infrastructure resources. With HPE Helion Development
Platform, your team can focus on delivering business outcomes.
Analytics and data management services for IoT
We recognize that the journey to becoming a data-driven organization and capitalizing on the IoT market
opportunity while managing the challenges and risks can be daunting. As one of the world’s leading services
organizations, HPE Enterprise Services has more than 50 years of experience building a strong reputation of
industry expertise helping our clients to engage better with their customers, manage risk, and win with the
explosive growth of data.
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24. The HPE Analytics and Data Management practice provides an advisory-led transformation that bridges
traditional business intelligence with new Big Data technologies and practices—enabling enterprises to become
data-driven and agile, maximizing business outcomes across all industries. With 3,500 consultants and 1,200
data scientists and analytics professionals, Hewlett Packard Enterprise has the depth of knowledge, industry
expertise, and breadth of capabilities globally to help companies capture the full benefit of IoT.
Hewlett Packard Labs for IoT
Data growth is exploding with IoT and it will eventually be impossible for current computing architectures to
keep up and make sense of all the data. To combat this future data explosion, Hewlett Packard Labs is breaking
down barriers and inventing completely new computing paradigms that will change the way we manage and
interact with information. Known as “The Machine,” Hewlett Packard Labs is creating technology that will make
it possible to manage millions of computing nodes, perform exabyte-scale calculations, and deliver a fully
dimensional data experience that is intuitive and collaborative for the future IoT.
However, for many IoT use cases, it may not be possible to move data off the edge into a centralized
machine. For this reason, we are also exploring how to take “The Machine” out of the core and shrink it down
for computing on the edge. Known as distributed mesh computing, the approach involves sprinkling novel
low-power, large-memory compute nodes throughout a network so that data can be stored, processed,
and analyzed locally throughout a mesh. The nodes then proactively collaborate to harness the same level
of intelligence collectively that might be availed from a large data lake, but without the costs or challenges
of having to centralize the data in one location. As the proliferation of data from IoT increases, innovations
from the Hewlett Packard Labs will enable the necessary quantum leaps in performance and efficiency while
lowering costs and improving security—ensuring that the future IoT will continue to enable new business
outcomes, even at unprecedented scale.
“We believe that the implications of the Machine will be
dramatic. When we say, HP (now Hewlett Packard Enterprise)
invents the future, this is what we’re talking about.”
– Meg Whitman, CEO and Chairwoman of Hewlett Packard Enterprise
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25. Partner ecosystem for IoT
The Internet of Things can enable new business outcomes and value creation, but the value cannot
be delivered by one technology provider alone. Maximizing the benefit and minimizing the risks to
IoT is a team sport. That is why Hewlett Packard Enterprise has an open ecosystem and has created
strategic partnerships and integrated solutions throughout the technology stack. Building on our
technologies and services in the HPE IoT Reference Model, our ecosystem partners integrate their products
and services so that together we can help our customers be successful.
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Figure 3: HPE’s breadth of solutions for the IoT
HP’s technologies and services empowering a data driven organization
Devices
The HPE technologies and services empowering a data-driven organization
Network OutcomesEdge Core
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26. Where to start
The world is changing. The Internet of Things can create countless opportunities for the transformation
of businesses and industries. In this idea economy, companies need to become data-driven organizations,
leveraging new and old data to create new value and insights that will lead to breakout growth.
We recommend starting first with identifying your desired business outcomes both near term and in the
more distant future. Then, you will want to develop a Big Data and IoT strategy by understanding your
existing data and the devices, equipment, and machines critical to your operations. Be inquisitive starting
with these questions:
• What impact do devices and equipment have on our business and operations?
• How does our organization incorporate sensor data today and are we getting the most value possible?
• What does integrated intelligence mean for our business?
• How can we integrate contextual insights from disparate data sources to achieve integrated intelligence
across our business operations?
• How would more instrumentation or better insights and intelligence of our assets impact products and services?
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27. You’ll need a partner that understands the value, the business opportunities, and the end-to-end challenges
and has the breadth and depth of technologies, services, and ecosystem to solve these challenges and help
deliver the value. Thankfully, you have Hewlett Packard Enterprise.
Reach out to us and we will be glad to partner with you to help design, develop, and implement IoT solutions
for your business.
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Resources
Papers
• IoT—Turning ordinary things into extraordinary business outcomes—Brochure
• How to Choose an IT Platform to Empower Your Internet of Things—White paper
• Connect-and-Protect: Building a trust based IoT for business-critical applications—Technical
white paper
• Capitalize on the untapped potential of sensor data—Business white paper
• Sensor data analytics—Brief
• Put Data to Work
• Be a smart city—Business white paper
• HPE Internet of Things Smart Metering—Solution overview
Videos
• If Machines Could Talk
• IoT Highlights from Mobile World Congress 2015
Learn more at
hpe.com/info/iot
hpe.com/info/bigdata
Follow us @HPE_BigData
© Copyright 2015–2016 Hewlett Packard Enterprise Development LP. The information contained herein is subject to change without notice. The only warranties
for Hewlett Packard Enterprise products and services are set forth in the express warranty statements accompanying such products and services. Nothing
herein should be construed as constituting an additional warranty. Hewlett Packard Enterprise shall not be liable for technical or editorial errors or omissions
contained herein.
4AA6-0506ENW, February 2016, Rev. 1
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